• Title/Summary/Keyword: statistical forecast model

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N-point modified exponential model for household projections in Korea using multi-point register-based census data

  • Saebom Jeon;Tae Yeon Kwon
    • Communications for Statistical Applications and Methods
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    • v.31 no.4
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    • pp.377-391
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    • 2024
  • Accurate household projections are essential for sectors such as housing supply and tax policy planning, given the rapid social changes like declining birthrates, an aging population, and a rise in single-person households that impact household size and type. Korea introduced its first register-based census in 2015, transitioning from five-year general survey-based approach to an annual administrative data-based census. This change in census allows for more frequent and effective capturing the rapid demographic shifts and trends. However, this change in census has caused challenges in future projection by the existing household projection model due to the rapid dynamics. This paper proposes a new household projection method, the N-point Modified Exponential Model (MEM), that accurately reflects register-based census data and mitigates the impact of rapid demographic changes, in three types: the Weighted N-point MEM, the Regression-based N-point MEM, and the Rolling Weighted N+point MEM. Using register-based census data from 2016 to 2020 to forecast household headship rates by age, household size, and household type to 2051, the N-point modified exponential model outperformed the existing model in both long- and short-term forecast accuracy, suggesting its suitability as a future household projection model for Korea.

Prediction of extreme PM2.5 concentrations via extreme quantile regression

  • Lee, SangHyuk;Park, Seoncheol;Lim, Yaeji
    • Communications for Statistical Applications and Methods
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    • v.29 no.3
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    • pp.319-331
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    • 2022
  • In this paper, we develop a new statistical model to forecast the PM2.5 level in Seoul, South Korea. The proposed model is based on the extreme quantile regression model with lasso penalty. Various meteorological variables and air pollution variables are considered as predictors in the regression model, and the lasso quantile regression performs variable selection and solves the multicollinearity problem. The final prediction model is obtained by combining various extreme lasso quantile regression estimators and we construct a binary classifier based on the model. Prediction performance is evaluated through the statistical measures of the performance of a binary classification test. We observe that the proposed method works better compared to the other classification methods, and predicts 'very bad' cases of the PM2.5 level well.

EMD based hybrid models to forecast the KOSPI (코스피 예측을 위한 EMD를 이용한 혼합 모형)

  • Kim, Hyowon;Seong, Byeongchan
    • The Korean Journal of Applied Statistics
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    • v.29 no.3
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    • pp.525-537
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    • 2016
  • The paper considers a hybrid model to analyze and forecast time series data based on an empirical mode decomposition (EMD) that accommodates complex characteristics of time series such as nonstationarity and nonlinearity. We aggregate IMFs using the concept of cumulative energy to improve the interpretability of intrinsic mode functions (IMFs) from EMD. We forecast aggregated IMFs and residue with a hybrid model that combines the ARIMA model and an exponential smoothing method (ETS). The proposed method is applied to forecast KOSPI time series and is compared to traditional forecast models. Aggregated IMFs and residue provide a convenience to interpret the short, medium and long term dynamics of the KOSPI. It is also observed that the hybrid model with ARIMA and ETS is superior to traditional and other types of hybrid models.

A Study on the Usefulness of EVA as Hospital Bankruptcy Prediction Index (병원도산 예측지표로서 EVA의 유용성)

  • 양동현
    • Health Policy and Management
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    • v.12 no.3
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    • pp.54-76
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    • 2002
  • This study investigated how much EVA which evaluate firm's value can explain hospital bankruptcy prediction as a explanatory variable including financial indicators in Korea. In this study, artificial neural network and logit regression which are traditional statistical were used as the model for bankruptcy prediction. Data used in this study were financial and economic value added indicators of 34 bankrupt and -:4 non-bankrupt hospitals from the Database of Korean Health Industry Development Institute. The main results of this study were as follows: First, there was a significant difference between the financial variable model including EVA and the financial variable model excluding EVA in pre-bankruptcy analysis. Second, EVA could forecast bankruptcy hospitals up to 83% by the logistic analysis. Third, the EVA model outperformed the financial model in terms of the predictive power of hospital bankruptcy. Fourth, The predictive power of neural network model of hospital bankruptcy was more powerful than the legit model. After all the result of this study will be useful to future study on EVA to evaluate bankruptcy hospitals forecast.

Flood Stage Forecasting using Class Segregation Method of Time Series Data (시계열자료의 계층분리기법을 이용한 하천유역의 홍수위 예측)

  • Kim, Sung-Weon
    • 한국방재학회:학술대회논문집
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    • 2008.02a
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    • pp.669-673
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    • 2008
  • In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

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Analysis of Price Forecasting and Goodness-of-Fit of the Metals Extracted from Deep Seabed Manganese Nodules (심해저 망간단괴에서 추출되는 금속가격 예측 및 적합도 분석)

  • Kwon, Suk-Jae;Jeong, Sun-Young
    • Ocean and Polar Research
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    • v.36 no.4
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    • pp.505-514
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    • 2014
  • The development of deep seabed manganese nodules has been carried out with the aim of commercial development in 2023. It is important to forecast the price of the four metals (copper, nickel, cobalt, and manganese) extracted from manganese nodules because price change is a criterion for investment decision. The main purpose of the study is to forecast the price of four metals using the ARIMA model and VAR model, and calculate the MAPE to compare a goodness-of-fit between the two models. The estimated results of the two models reveal statistical significance and are in keeping with economic theory. The results of MAPE for goodness-of-fit show that the VAR model is between 0.1 and 0.2, and the ARIMA model is between 0.4 and 0.6. That is, the VAR model is better than the ARIMA model in forecasting changes in the price of metals.

Flood Stage Forecasting using Kohonen Self-Organizing Map (코호넨 자기조직화함수를 이용한 홍수위 예측)

  • Kim, Seong-Won;Kim, Hyeong-Su
    • Proceedings of the Korea Water Resources Association Conference
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    • 2007.05a
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    • pp.1427-1431
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    • 2007
  • In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

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Forecasting Korean housing price index: application of the independent component analysis (부동산 매매지수와 전세지수 예측: 독립성분분석을 활용한 분석)

  • Pak, Ro Jin
    • The Korean Journal of Applied Statistics
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    • v.30 no.2
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    • pp.271-280
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    • 2017
  • Real-estate values and related economics are often the first read newspaper category. We are concerned about the opinions of experts on the forecast for real estate prices. The Box-Jenkins ARIMA model is a commonly used statistical method to predict housing prices. In this article, we tried to predict housing prices by combining independent component analysis (ICA) in multivariate data analysis and the Box-Jenkins ARIMA model. The two independent components for both the selling price index and the long-term rental price index were extracted and used to predict the future values of both indices. In conclusion, it has been shown that the actual indices and the forecast indices using ICA are more comparable to the forecasts of the ARIMA model alone.

Drought Forecasting Using the Multi Layer Perceptron (MLP) Artificial Neural Network Model (다층 퍼셉트론 인공신경망 모형을 이용한 가뭄예측)

  • Lee, Joo-Heon;Kim, Jong-Suk;Jang, Ho-Won;Lee, Jang-Choon
    • Journal of Korea Water Resources Association
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    • v.46 no.12
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    • pp.1249-1263
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    • 2013
  • In order to minimize the damages caused by long-term drought, appropriate drought management plans of the basin should be established with the drought forecasting technology. Further, in order to build reasonable adaptive measurement for future drought, the duration and severity of drought must be predicted quantitatively in advance. Thus, this study, attempts to forecast drought in Korea by using an Artificial Neural Network Model, and drought index, which are the representative statistical approach most frequently used for hydrological time series forecasting. SPI (Standardized Precipitation Index) for major weather stations in Korea, estimated using observed historical precipitation, was used as input variables to the MLP (Multi Layer Perceptron) Neural Network model. Data set from 1976 to 2000 was selected as the training period for the parameter calibration and data from 2001 to 2010 was set as the validation period for the drought forecast. The optimal model for drought forecast determined by training process was applied to drought forecast using SPI (3), SPI (6) and SPI (12) over different forecasting lead time (1 to 6 months). Drought forecast with SPI (3) shows good result only in case of 1 month forecast lead time, SPI (6) shows good accordance with observed data for 1-3 months forecast lead time and SPI (12) shows relatively good results in case of up to 1~5 months forecast lead time. The analysis of this study shows that SPI (3) can be used for only 1-month short-term drought forecast. SPI (6) and SPI (12) have advantage over long-term drought forecast for 3~5 months lead time.

The Impact of Satellite Observations on the UM-4DVar Analysis and Prediction System at KMA (위성자료가 기상청 전지구 통합 분석 예측 시스템에 미치는 효과)

  • Lee, Juwon;Lee, Seung-Woo;Han, Sang-Ok;Lee, Seung-Jae;Jang, Dong-Eon
    • Atmosphere
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    • v.21 no.1
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    • pp.85-93
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    • 2011
  • UK Met Office Unified Model (UM) is a grid model applicable for both global and regional model configurations. The Met Office has developed a 4D-Var data assimilation system, which was implemented in the global forecast system on 5 October 2004. In an effort to improve its Numerical Weather Prediction (NWP) system, Korea Meteorological Administration (KMA) has adopted the UM system since 2008. The aim of this study is to provide the basic information on the effects of satellite data assimilation on UM performance by conducting global satellite data denial experiments. Advanced Tiros Operational Vertical Sounder (ATOVS), Infrared Atmospheric Sounding Interferometer (IASI), Special Sensor Microwave Imager Sounder (SSMIS) data, Global Positioning System Radio Occultation (GPSRO) data, Air Craft (CRAFT) data, Atmospheric Infrared Sounder (AIRS) data were assimilated in the UM global system. The contributions of assimilation of each kind of satellite data to improvements in UM performance were evaluated using analysis data of basic variables; geopotential height at 500 hPa, wind speed and temperature at 850 hPa and mean sea level pressure. The statistical verification using Root Mean Square Error (RMSE) showed that most of the satellite data have positive impacts on UM global analysis and forecasts.